Ion Homeostasis in Rhythmogenesis: The Interplay Between Neurons and Astroglia
Bibliographic record
Abstract
Proper function of all excitable cells depends on ion homeostasis. Nowhere is this more critical than in the brain where the extracellular concentration of some ions determines neurons' firing pattern and ability to encode information. Several neuronal functions depend on the ability of neurons to change their firing pattern to a rhythmic bursting pattern, whereas, in some circuits, rhythmic firing is, on the contrary, associated to pathologies like epilepsy or Parkinson's disease. In this review, we focus on the four main ions known to fluctuate during rhythmic firing: calcium, potassium, sodium, and chloride. We discuss the synergistic interactions between these elements to promote an oscillatory activity. We also review evidence supporting an important role for astrocytes in the homeostasis of each of these ions and describe mechanisms by which astrocytes may regulate neuronal firing by altering their extracellular concentrations. A particular emphasis is put on the mechanisms underlying rhythmogenesis in the circuit forming the central pattern generator (CPG) for mastication and other CPG systems. Finally, we discuss how an impairment in the ability of glial cells to maintain such homeostasis may result in pathologies like epilepsy and Parkinson's disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".